Proposed Analysis Framework Research Articles

A forward modelling approach for the estimation of oxygen extraction fraction by calibrated fMRI

The measurement of the absolute rate of cerebral metabolic oxygen consumption (CMRO2) is likely to offer a valuable biomarker in many brain diseases and could prove to be important in our understanding of neural function. As such there is significant interest in developing robust MRI techniques that can quantify CMRO2 non-invasively. One potential MRI method for the measurement of CMRO2 is via the combination of fMRI and cerebral blood flow (CBF) data acquired during periods of hypercapnic and hyperoxic challenges. This method is based on the combination of two, previously independent, signal calibration techniques. As such analysis of the data has been approached in a stepwise manner, feeding the results of one calibration experiment into the next. Analysing the data in this manner can result in unstable estimates of the output parameter (CMRO2), due to the propagation of errors along the analysis pipeline. Here we present a forward modelling approach that estimates all the model parameters in a one-step solution. The method is implemented using a regularized non-linear least squares approach to provide a robust and computationally efficient solution. The proposed framework is compared with previous analytical approaches using modelling studies and in vivo acquisitions in healthy volunteers (n=10). The stability of parameter estimates is demonstrated to be superior to previous methods (both in vivo and in simulation). In vivo estimates made with the proposed framework also show better agreement with expected physiological variation, demonstrating a strong negative correlation between baseline CBF and oxygen extraction fraction. It is anticipated that the proposed analysis framework will increase the reliability of absolute CMRO2 measurements made with calibrated BOLD.

Automatic Detection of Potential Automation Surprises for ADEPT Models

This paper describes how to automatically detect potential automation surprises in interactive systems, within a rapid automation interface design tool named ADEPT. The proposed analysis method in this paper is based on a conformance relation, called full-control , between the model of the actual system and a mental model of it, that is, its behavior as perceived by the operator. The method can, among other things, automatically generate a so-called minimal full-control mental model for a given system. Systems are well designed if they can be described by relatively simple mental models for their operators, which can be assessed with the minimal full-control mental model generation algorithms. During the generation, potential automation surprises are detected and highlighted with execution examples that may lead to confusion. The analysis methods are based on an enriched version of labeled transition systems to describe the system and mental models. In order to be able to integrate the analysis method within ADEPT, a semantics for ADEPT models makes it possible to translate them into enriched LTSs. The proposed translation is automated for a specified class of ADEPT models that are characterized and defined in this paper. A case study demonstrates the proposed analysis framework and informs how the integration with ADEPT can be improved.

Object-Based Building Change Detection from a Single Multispectral Image and Pre-Existing Geospatial Information

AbstractMultispectral images of very high spatial resolution and vector data from geospatial databases, such as cadastral maps, are among the cost-effective and broadly available geodata in urban environments. Therefore, we aim to address building change detection based on pre-existing building footprint information and a single very high resolution multispectral image. An object-based classification methodology was developed that employs advanced scalespace filtering, unsupervised clustering, and knowledge-based classification. The developed framework effectively integrates prior vector data and multispectral observations, through incorporating the prior knowledge into the training process and defining the proper object-based classification rules. The methodology successfully identified important building changes, which were validated by employing the vector information of a building geodatabase and a QuickBird image acquired in 2003 and 2007, respectively, over urban regions in the city of Thessaloniki, Greece. The performed quantitative and qualitative evaluation indicated that the proposed analysis framework can detect the new buildings with high accuracy rates and, to a lesser degree, their exact shape and size.

Reduction of complex energy-integrated process networks using graph theory

This paper focuses on the analysis of complex (multi-loop) energy-integrated process networks. Simple (single-loop) energy-integrated networks (comprising of large energy recycle or throughput) with two-time scale dynamics are the building blocks for such complex networks. The modular structure of these complex networks lends them to a graph theoretic analysis, whereby weak and strong connections between process units arising from time scale separation are identified from structural information. Subsequently, a graph-theoretic framework for network analysis and control is developed, and connecting links are built to an equivalent analysis using singular perturbations. The proposed analysis framework is illustrated via application to a representative complex process network.

On Studying the Impact of Uncertainty on Behavior Diffusion in Social Networks

Unlike traditional epidemic virus spreading, behavior diffusion in social networks is conducted by rational users, who would make strategic choices instead of being randomly infected with some probability. Specifically, individuals always try to maximize their utilities through rationally selecting specific behaviors (adopting a new product, or spreading a rumor, etc.). However, utility obtained by an individual, naturally contains uncertainty, and it may stem from two sources: users' imperfect and incomplete knowledge about others, and the inherently stochastic property in human behavior. Thus, it is imperative to model and analyze the diffusion pattern under the resulting uncertainty in social networks, which, however, has not yet been deeply examined by the existing works. This paper deeply explores the pattern of gossip diffusion in social networks when uncertainty exists in users' decision making. In detail, the innovative results provided in this paper are: first, inspired by random utility theory, we formulate the diffusion model based on mixed logit model that allows for user's uncertainty in determining whether to adopt a specific strategy; second, the formal analysis framework characterizing the diffusion process is derived through the approximation method of mean field theory; finally, we explore the extensive applicability of our proposed analysis framework through modeling rumor diffusion in social networks as a coordination game. Our findings are, for various structural characteristics, small uncertainty can significantly speed up the diffusion of gossip; furthermore, social networks with scale-free property can facilitate the gossip diffusion in the easiest way, but, the range of uncertainty factor that can maximize gossip diffusion is the smallest. The obtained results perfectly comply with the philosophical saying about rumor diffusion in real social life: easy come, easy go.

Analysis of fitness landscape modifications in evolutionary dynamic optimization

In this work, discrete dynamic optimization problems (DOPs) are theoretically analysed according to the modifications produced in the fitness landscape during the optimization process. Using the proposed analysis framework, the following DOPs are analysed: problems generated by the XOR DOP generator, three versions of the dynamic 0–1 knapsack problem, one problem involving evolutionary robots in dynamic environments, and the random dynamics NK-model. The XOR DOP generator creates benchmark DOPs from any binary static optimization problem, which allows to explore the properties of the static problem in a dynamic environment. Three types of transformations occurring in the fitness landscapes are observed in the DOPs analysed here. They are caused by: (i) permutation of solutions in the search space; (ii) duplication of solutions; and (iii) adding deviations to the fitness of a subset of solutions. The XOR DOP generator creates a special type of permutation that is not found in the other investigated DOPs. In this way, a new benchmark problem generator is proposed here based on the analysis performed, allowing to produce DOPs with six types of fitness landscape transformations, including those similar to the problems investigated in this paper. When compared to the XOR DOP generator, new algorithms can be tested and compared in a wider range of dynamic environments using the new generator. It is important to observe that some of the fitness transformations analysed here, like those caused by the duplication of solutions, are not currently explored in the evolutionary dynamic optimization area.

Using multitemporal Landsat imagery to monitor and model the influences of landscape pattern on urban expansion in a metropolitan region

Studying the interaction between landscape patterns and temporal land-use changes in a metropolitan area can improve understanding of the urbanization process. Multitemporal remote sensing imagery is widely used to map the urbanization-caused temporal land-use dynamics, which mainly appear as built-up growth. Remote sensing integrated with landscape metrics is also used to quantitatively describe the landscape pattern of the urban area in recent literature. However, few studies have focused on the interaction between the pattern and the process of urbanization in a metropolitan area. We propose a grid-based framework to analyze the influence of the landscape pattern on the built-up growth by using the multitemporal Landsat imagery. Remote sensing classification method is used to obtain thematic land-use maps. Built-up growth is then extracted from the multitemporal classification results by a postclassification change detection. Landscape pattern, which is quantitatively described by landscape metrics, is derived from the thematic land-use maps. A grid-based method is used to analyze the spatial variation of landscape pattern and its related built-up growth. Finally, the spatial relationship between the landscape pattern and the built-up growth characters is assessed and modeled by using the mathematical regression method. The present study shows that an apparent correlation between landscape pattern and built-up growth exists. The correlation reflects the inherent influences of landscape pattern on urban expansion. The landscape pattern indicates the land development stage, while the urbanization stage determines the speed and style of the following built-up growth. Scales, including temporal scale and spatial scale, are important to modeling the landscape pattern effects on the built-up growth. The proposed analysis framework is efficient in detecting and modeling the landscape pattern effects on the built-up growth.

Analysis and Evaluation on NBTI-Induced Circuit Aging

Aging effects degrade circuit performance with time, inducing reliability problem. Accurate prediction of circuit aging can help designer determine the reasonable design margin, avoiding the over-design of the circuit. Based on the physical understanding of aging mechanism, an analysis framework is proposed to predict NBTI-induced circuit aging. The analysis framework starts at the worst case prediction, which assumes the extremely operational conditions. Then, the impacts of different workloads and logic topology of the circuit on the aging-induced degradation are incorporated into the analysis framework to make the predicted result be closer to the practical scenario. Experimental results demonstrate that the effectiveness of the proposed analysis framework.

An Analysis Framework for Identifying Usability Requirements in Mobile Application Development

People reasons to choose an application is for the functionalities offered. Though, the way in which the functions are performed will also have a significant impact to the user’s choice. Software usability is one of the crucial factors that can influence user acceptance. It assesses the extent of which software facilitates users in utilizing the offered functions easily and effectively. Usability requirements are associated and complement the functional requirements. However, it is common that the usability requirements are captured at the design stage of software development due to its characteristic. Essentially, it should be identified at the earlier stage (i.e., requirements stage) to ensure the success of the software product. Therefore, we explore the potential benefits of Soft System Methodology (SSM) in identifying usability requirements at the requirements stage. The proposed SSM-based framework is applied in preliminary analysis, in which the results are useful to inform the next stage of the development process. The practical benefits of the proposed analysis framework are demonstrated in a case study. The framework would be beneficial particularly for mobile software developers in improving the quality of the mobile applications.

품질보증 이슈 조기감지 시스템의 경제성 평가

An early detection system for warranty issues periodically collects customers' claim data and automatically reports alarms about emerging issues based on statistical algorithms. It helps companies to reduce an issue definition time and save the handling cost of warranty claims. This paper provides an evaluation framework to validate the economic effect of an early detection system project. For this purpose, we present economical index of a project with explicit formulas such as ROI(return on investment), PP(payback period), NPV(net present value), PI(profitability index) and IRR(internal rate of return) and analyze the sensitivities of the index according to the variation of project input parameters. The proposed analysis framework is expected to be used for evaluating economic values of various system integration projects.

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA results are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the results of basic DEA models. The paper formally shows how the results of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides DEA results which are consistent with the framework and are ready-to-analyze with data mining tools, thanks to their specially designed table-based structures. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.

A Framework for Analyzing and Comparing Information Literacy Policies in European Countries

Information literacy was conceived originally as a policy goal in 1974, when Paul Zurkowski expressed the need for establishing a major national program to achieve universal information literacy. Despite such early recognition of its political imprinting, the policy dimension of information literacy has been given scant attention in the academic literature. In order to pave the way toward concrete and coordinated policy measures, this article proposes a stratification of the information literacy discourse into three different perspectives of analysis: (a) sociopolitical perspective: analysis of information literacy as a policy goal (Education to Information); (b) disciplinary perspective: analysis of information literacy as a form of study of information (Culture of Information); (c) cognitive perspective: analysis of information literacy as a form of personal competence (Information Skills). Focusing on the sociopolitical perspective, this article moves on to discuss the view that information literacy is a policy goal crossing the borders of both information and education domains, in that it is an information policy issue that also enters the sphere of influence of education policies. The next sections propose a framework for analyzing and comparing information literacy policies in European countries. The overall aim is to apply a grid of analysis based on a set of variables, suitably defined in order to give a measure of what we call the IL-readiness of a country. Finally, the application of the proposed analysis framework leads to the identification of different policy axes for information literacy.

Analyzing the Solutions of DEA Through Information Visualization and Data Mining Techniques: SmartDEA Framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.

Intertrial coherence and causal interaction among independent EEG components

Over the past few years there has been an increased interest in studying the underlying neural mechanism of attention and cognitive brain activity. This paper aims towards identifying and analyzing distinct responses in an auditory working memory paradigm, as independent components with variable latency, frequency and phase characteristics. The event-related nature of components (either phase or non-phase-locked) over multiple trials is thoroughly examined through intertrial coherence measures. Furthermore, the functional coupling of independent components is investigated through the concept of partial directed coherence depicted as a directed graph. Using these tools, the paper compares issues of activation, connectivity and directionality in the synchronization maps of two populations, of control and Alzheimer's subjects. The results on real data from an oddball experiment verify and further enhance the findings of previous studies and illustrate the potential of the proposed analysis framework.

A study on the flexibility of enzyme active sites

BackgroundA common assumption about enzyme active sites is that their structures are highly conserved to specifically distinguish between closely similar compounds. However, with the discovery of distinct enzymes with similar reaction chemistries, more and more studies discussing the structural flexibility of the active site have been conducted.ResultsMost of the existing works on the flexibility of active sites focuses on a set of pre-selected active sites that were already known to be flexible. This study, on the other hand, proposes an analysis framework composed of a new data collecting strategy, a local structure alignment tool and several physicochemical measures derived from the alignments. The method proposed to identify flexible active sites is highly automated and robust so that more extensive studies will be feasible in the future. The experimental results show the proposed method is (a) consistent with previous works based on manually identified flexible active sites and (b) capable of identifying potentially new flexible active sites.ConclusionsThis proposed analysis framework and the former analyses on flexibility have their own advantages and disadvantage, depending on the cause of the flexibility. In this regard, this study proposes an alternative that complements previous studies and helps to construct a more comprehensive view of the flexibility of enzyme active sites.

Analysis and Design of a Robust Power System Damping Controller Considering Time Delay

This article presents a detailed analysis and design of a static var compensator damping controller for a power system. The drawbacks of the conventional approach to the design of damping controllers are demonstrated. It is found that the interaction between the static var compensator and other system control modes can be worsened by time delay. The relation between time delay uncertainty and control interaction is revealed, using the proposed analysis framework, and is also demonstrated using non-linear simulations. A robust controller based on the μ-synthesis method is designed to overcome the design challenges.

Fatigue Reliability Analysis of the Stay Cables of Cable-Stayed Bridge under Combined Loads of Stochastic Traffic and Wind

The existent studies on cable stays fatigue for the serviced cable-stayed bridge generally only considered traffic or wind load action respectively. The long span cable-stayed bridges are very sensitive to wind load, so the fatigue estimation of cable stays considering traffic and wind load simultaneously is very important for the bridge safety. In the present research, taking an actual bridge as an example, based on linear cumulative damage theory, fatigue reliability of cable stays is analyzed under combined load of vehicles and wind. Firstly, based on the long-term traffic survey and wind speed data, traffic and wind load probability distribution models for the bridge are built respectively. Secondly, an intensive computational work is performed to obtain stress time history of the stay cables in the typical time block by running self-compiled Bridge-Vehicle-Wind interaction dynamic response analysis program. Thirdly, the stress result is updated in accordance with traffic growth and extreme wind speed changing in service period. The stress amplitude and frequency are attained by rain-flow cycle counting method. Finally, the fatigue damage limit state function of cable stays is proposed based on linear cumulative damage theory, and solved by Monte-Carlo method. The analysis result shows that the effect of buffeting wind load on the fatigue reliability of cable stays is significant, the influence degree increases generally in accordance with the order from short cable to long cable. The fatigue life of cable under designed safety probability reduces by the range from 2% to 63%, average 50% compared to only considering traffic load. So fatigue assessment of stay cables should take traffic and wind loads together into account. The proposed analysis framework offers a referenced fatigue assessment approach for conventional long span bridges.

Inelastic analysis of granular interfaces via computational contact homogenization

AbstractThe macroscale response of granular contact interfaces is investigated. In order to circumvent the difficulties associated with a direct resolution of such heterogeneous contact problems, where highly mobile particles residing between a deformable body and a rigid surface govern the microscale dynamics, a space–time contact homogenization methodology is developed. The overall approach is based on a separation of spatial as well as temporal scales and proposes an idealized purely frictional macroscale response. The induced macroscale dissipation is directly associated with the microscale dissipation mechanisms due to (i) an inelastic constitutive response for the boundary layer of the deformable body and (ii) frictional interaction among the components of the three‐body contact system. The consequences of a viscoelastic boundary layer that sustains damage due to highly localized deformation in the vicinity of the particles are investigated extensively within a fully nonlinear computational setting that accounts for incompressibility. The effective coefficient of friction that is induced by the homogenization methodology as the fundamental macroscale observable is found to be of a non‐Amontons as well as a non‐Coulomb type. The proposed analysis framework is amenable to a multiscale implementation within a coupled micro–macro approach and yields insight into the macroscopic dynamics of similar heterogeneous interfaces with varying degrees of mobility associated with the roughness features. Copyright © 2010 John Wiley & Sons, Ltd.

Throughput Estimation of Downlink Packet Access Systems Based on a Point Mass Approximation Concept

Although the system throughput of a wireless communication system considering multiple users' geographical distribution on a 2-D plane and various traffic characteristics is an important performance metric, its mathematical analysis is very difficult due to the nonindependent identically distributed distribution of the received signal-to-noise ratio (SNR) of each mobile terminal and the complex dynamics of the traffic patterns. Therefore, complicated system-level simulations have been the only approach to evaluate the system throughput of downlink packet-access systems. In this paper, we propose an efficient analytical approach to estimate the system throughput of downlink packet-access systems by using point mass approximation, which is widely used in physics and mechanics. The proposed analysis framework enables the mathematical analysis of the system throughput performance by considering various positions of mobile terminals and traffic patterns without the complicated and heavy system-level simulations. Although the proposed approach has a tradeoff relation between computation complexity and accuracy, we show that it is possible to obtain an effective accuracy with moderate complexity using the proposed analytical approach.

Performance quantification for distribution system restoration

Service restoration is an important issue for power system operations. In this paper, we present an effective approach for analyzing restoration plans quantitatively and systematically by using the Analytical Hierarchy Process (AHP) based Fuzzy‐Grey approach. The AHP was used for assessing the weight of each objective function in a quantitative and rational way. The Fuzzy‐Grey algorithm was adopted to deal with imprecise linguistic descriptions existing in operators’ heuristic rules and to quantify the ranking of each feasible restoration plan. The proposed analysis framework can be applied to assist system operators in making decisions during system reconfiguration. Three study cases were used to demonstrate the effectiveness of the proposed approach. Results show that the proposed quantitative evaluation model is useful to evaluate different restoration plans by ranking them by simple computations.